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中华临床医师杂志(电子版)

中华临床医师杂志(电子版) ›› 2024, Vol. 18 ›› Issue (01) : 41 -56. doi: 10.3877/cma.j.issn.1674-0785.2024.01.008

临床研究

pMMR/MSS型结肠癌免疫治疗效果及预后标志物研究
武文晓1, 张大奎2, 孙志刚2, 韩子翰2, 陈少轩2, 侯智勇2, 孙白龙2, 介建政1,†()   
  1. 1. 100029　北京，中日友好医院（中日友好临床医学研究所）/北京协和医学院/中国医学科学院;100029　北京，中日友好医院普外科·结直肠外科
    2. 100029　北京，中日友好医院普外科·结直肠外科
  • 收稿日期:2023-11-24 出版日期:2024-01-15
  • 通信作者: 介建政
  • 基金资助:
    中央高水平医院临床科研业务费资助(2023-NHLHCRF-YYPPLC-ZR-13)

Novel biomarker for immunotherapy and prognostic in colon cancer with mismatch repair proficiency or microsatellite stability

Wenxiao Wu1, Dakui Zhang2, Zhigang Sun2, Zihan Han2, Shaoxuan Chen2, Zhiyong Hou2, Bailong Sun2, Jianzheng Jie1,†()   

  1. 1. China-Japan Friendship Hospital (Institute of Clinical Medical Sciences), Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100029, China;Department of Colorectal Surgery, China-Japan Friendship Hospital, Beijing 100029, China
    2. Department of Colorectal Surgery, China-Japan Friendship Hospital, Beijing 100029, China
  • Received:2023-11-24 Published:2024-01-15
  • Corresponding author: Jianzheng Jie
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引用本文：

武文晓, 张大奎, 孙志刚, 韩子翰, 陈少轩, 侯智勇, 孙白龙, 介建政. pMMR/MSS型结肠癌免疫治疗效果及预后标志物研究[J]. 中华临床医师杂志(电子版), 2024, 18(01): 41-56.

Wenxiao Wu, Dakui Zhang, Zhigang Sun, Zihan Han, Shaoxuan Chen, Zhiyong Hou, Bailong Sun, Jianzheng Jie. Novel biomarker for immunotherapy and prognostic in colon cancer with mismatch repair proficiency or microsatellite stability[J]. Chinese Journal of Clinicians(Electronic Edition), 2024, 18(01): 41-56.

目的

旨在建立可预测MSS/pMMR结直肠癌患者免疫治疗效果的新型生物标志物并探索可以逆转其免疫抑制的治疗方案。

方法

从癌症基因组图谱（TCGA）数据库中纳入261例MSS/pMMR结肠癌患者。根据肿瘤组织的免疫浸润情况对纳入人群通过无监督聚类分组，用单因素Cox回归分析和LASSO-Cox分析对组间的差异表达基因进行筛选并构建预测模型，用外部队列进行验证。最后，利用癌症药物敏感性基因组学数据库（GDSC）和CMap数据库探索增敏免疫治疗的潜在方法。

结果

建立了包含20个基因的预测模型，根据肿瘤的基因表达特征计算风险分数，将纳入人群分成高风险和低风险组。20个基因的表达特征是影响预后的独立因素并可以很好地预测免疫治疗效果。药物基因组学分析显示5-羟色胺受体阻滞剂联合免疫治疗可以增强高风险组患者的治疗效果。

结论

在MSS/pMMR结肠癌中，20基因模型可预测生存和免疫治疗效果，是一个具有应用前景的生物标志物。

关键词: 结肠癌, 免疫治疗, 微卫星稳定, 生物标志物, 预后
Objective

This study aimed to establish a predictive signature for identifying candidates for immunotherapy in patients with pMMR/MSS and explore potential approaches to convert their immunosuppressive conditions.

Methods

A total of 261 colon cancer patients with pMMR/MSS from The Cancer Genome Atlas colon cancer (TCGA-COAD) dataset were dissected based on the immune-cell infiltration profile using unsupervised clustering algorithm. The differentially expressed genes were employed to construct a predictive signature through univariate Cox regression and least absolute shrinkage and selection operator (LASSO)-Cox analyses. Another two independent cohorts were subjected to validation to assess the robustness of the signature. Then, comprehensive analyses of immune status as well as biological pathway and functional enrichment analyses were performed to unveil the underlying mechanisms behind the signature. Finally, the Genomics of Drug Sensitivity in Cancer (GDSC) and Connectivity Map (CMap) databases were used to explore the potential approaches to enhance the effect of immunotherapy.

Results

A 20-gene signature was constructed. Two risk subsets were categorized based on the risk scores calculated by tumor expression profiles of the 20 genes. Notably, the signature was an independent prognostic factor and exhibited a powerful capacity for survival and immunotherapy response prediction. Additionally, we observed a significant difference in immune-cell infiltration profile between the two risk groups. The functional enrichment analyses indicated significant enrichment of immune-related pathways and inflammatory processes in low-risk patients. Moreover, high-risk group exhibited higher IC50 values for certain chemotherapy drugs, such as cisplatin and 5-fluorouracil. Pharmacogenomics analysis illustrated that serotonin receptor antagonist combined with immunotherapy may convert the insensitivity of immunotherapy in high-risk patients.

Conclusion

The 20-gene signature is a promising biomarker to predict survival outcome and response to immunotherapy in colon cancer patients with pMMR/MSS.

Key words: Colonic neoplasms, Immunotherapy, Microsatellite stability, Biomarker, Prognosis
图1 研究流程图
图2 pMMR/MSS型结肠癌的免疫细胞浸润情况。图a为将样本分为两个类别时的一致性矩阵热图；图b为不同类簇聚类分析结果的累积分布函数；图c为两个亚组之间28种免疫细胞浸润情况的组间比较结果
图3 pMMR/MSS型结肠癌患者预后模型构建。图a为LASSO回归中均方误差随Log（λ）变化图以及回归系数随Log（λ）的变化曲线图，通过回归模型筛选得到20个基因；图b为TCGA结肠癌队列的风险分数分布
图4 20个基因与总生存（OS）和风险分数的相关性。图a为森林图显示20个基因和结肠癌总生存的单因素Cox回归分析结果；图b为20个基因与风险分数的相关性
图5 TCGA结肠癌队列和验证队列中，模型的预后价值。图a为TCGA队列中不同风险分组的生存曲线；图b为IMvigor210队列中不同风险分组的生存曲线；图c为DFCI队列中不同风险分组的生存曲线；图d为森林图显示TCGA结肠癌队列多因素Cox回归模型中风险分数和总生存的关系；图e为列线图整合风险分数、年龄以及肿瘤分期来预测TCGA队列结肠癌患者的3年和5年生存率；图f为校正曲线显示TCGA结肠癌队列3年和5年总生存率；图g为TCGA队列中风险分数的时间依赖ROC；图h IMviogor210队列中风险分数的时间依赖ROC；图i为DFCI队列中风险分数的时间依赖ROC
图6 TCGA队列中高风险组与低风险组的突变景观。图a为瀑布图显示低风险组的突变特征；图b为高风险组的突变特征；图c为森林图展示低风险组和高风险组在突变频率最高的10个基因之间的差异
图7 TCGA队列中ESTIMATE算法的结果。图a为Estimate评分在高风险组和低风险组间的差异；图b为免疫评分在高风险组和低风险组间的差异；图c为肿瘤纯度评分在高风险组和低风险组间的差异
图8 pMMR/MSS型结肠癌中基于预后模型的风险分数与免疫细胞浸润情况的相关性。图a为热图显示基于EPIC，MCPcounter，QUANTISEQ，和TIMER算法的高风险组和低风险组的免疫细胞浸润情况；图b为基于CIBERSORT算法的免疫细胞浸润情况的组间差异
图9 TCGA队列中高风险组和低风险组间的免疫状态差异。图a为ssGSEA分析得到高风险组和低风险组的28种免疫细胞占比的组间差异比较；图b为免疫检查点基因在高风险组和低风险组的表达情况比较；图c为ssGSEA分析得到高风险组和低风险组的癌症-免疫循环相关基因集的富集水平的组间差异比较
图10 TCGA队列TIDE算法结果。图a为高风险组和低风险组间的TIDE评分比较；图b为TIDE算法预测的对免疫治疗有反应人群和无反应人群的风险分数差异
图11 外部验证队列中高风险组和低风险组中PD-L1治疗的效果。图a为IMvigor210队列高风险组和低风险组中不同治疗反应的人群的分布情况；图b为DFCI队列高风险组和低风险组中不同治疗反应的人群的分布情况；图c为IMvigor210队列中不同免疫表型人群间风险分数的比较
图12 TCGA队列高风险组和低风险组间IPS评分差异。图a为IPS评分在高风险组和低风险组间的差异；图b为IPS-PD1/PD-L1/PD-L2评分在高风险组和低风险组间的差异；图c为IPS-CTLA4评分在高风险组和低风险组间的差异；图d为IPS-PD1/PD-L1/PD-L2+CTLA4评分在高风险组和低风险组间的差异；图e为高风险组和低风险组间新抗原数的差异；图f为高风险组和低风险组间体细胞突变数的差异
图13 预测模型与药物敏感性的关系。图a为顺铂的IC50估计值在高风险和低风险组间的差异；图b为5-FU的IC50估计值在高风险和低风险组间的差异
表1 Cap分析结果
分数 化合物名称 描述 靶点 Moa
-95.21 SB-206553 5-羟色胺受体拮抗剂 HTR2B, HTR2C, HTR1A, HTR2A 5-羟色胺受体拮抗剂
-86.53 MST-312 端粒酶抑制剂 TERT 端粒酶抑制剂
-86.22 dipropyl-dopamine 多巴胺受体激动剂 多巴胺受体激动剂
-80.98 HLI-373 MDM抑制剂 MDM2 MDM抑制剂
-80.96 xanthohumol ATP酶抑制剂 CYP19A1, VCP ATP酶抑制剂
-76.66 thioperamide 组胺受体拮抗剂 HRH3, HRH4 组胺受体拮抗剂
-75.1 NF-449 嘌呤能受体拮抗剂 P2RX1 嘌呤能受体拮抗剂
-73.87 baeomycesic-acid 脂氧合酶抑制剂 脂氧合酶抑制剂
-72.27 oleanolic-acid G 蛋白偶联受体激动剂 G 蛋白偶联受体激动剂
-70.96 gamma-homolinolenic-acid 胆固醇抑制剂 PTGS1, PTGS2 胆固醇抑制剂，血小板聚集抑制剂，前列腺素受体激动剂，前列腺素受体兴奋剂
-70.68 NSC-94258 抗肿瘤 AKR1B1, CYP19A1, HSD17B1 抗肿瘤
-70.39 methyl-angolensate 细胞凋亡抑制剂 HTR2B 细胞凋亡抑制剂
图14 基于预测模型的生物学功能和通路富集分析。图a为高风险组和低风险组的KEGG通路富集情况（log10P-value>0表示在高风险组中富集）；图b为高风险组的GO分析富集情况；图c为低风险组的GO分析富集情况
图15 GSEA富集分析结果。图a为TCGA队列中低风险组的基因集富集分析结果；图b为TCGA队列高风险组的基因集富集分析结果
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